For a given body of media content, such as online videos, digital music, electronic books, news websites, and other digital media, a recommendation system can be used to provide suggestions that are tailored to the personal preferences and interests of a user. One type of recommendation is content-based recommendation, which is based on the similarity of various attributes of content items. These attributes may include, for example, “category,” “genre,” “actors,” “artists,” “description,” and so forth. The similarity of content items can be computed by measuring a distance between attributes using, for example, a Jaccard index. Depending on the importance of these attributes to people, as measured by their indicated preferences for the associated content, different attributes can be assigned relative weights, which are used to calculate the similarity between two or more content items. However, current approaches to determining attribute weights suffer from a number of shortcomings that can adversely affect the quality and accuracy of content-based recommendations.